R Notebook

Load Packages

Making Dataframes

color_a <- c("#58b5e1","#1c5b5a","#46ebdc","#1f4196","#e28de2","#818bd7","#e4ccf1","#82185f","#f849b6",'Mecklenburg = "#000000","#5e34bc","#b7d165","#30d52e","#ff5357")
color_na <- c("#1c5b5a","#46ebdc","#1f4196","#e28de2","#818bd7","#e4ccf1","#82185f","#f849b6","#000000","#5e34bc","#b7d165","#30d52e","#ff5357")
counties <- c('Anson', 'Cabarrus', 'Catawba', 'Chester', 'Cleveland', 'Gaston', 'Iredell', 'Lancaster', 'Lincoln', 'Mecklenburg', 'Rowan', 'Stanly', 'Union', 'York')

Error: unexpected symbol in:
"color_na <- c("#1c5b5a","#46ebdc","#1f4196","#e28de2","#818bd7","#e4ccf1","#82185f","#f849b6","#000000","#5e34bc","#b7d165","#30d52e","#ff5357")
counties <- c('Anson"

Demographics

Population

plot_ly(countypop, x=~YEAR, y=~CHANGE, color=~CTYNAME, type='scatter', mode='lines', colors=color_a)

Warning: Duplicate levels detected
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Duplicate levels detected
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Duplicate levels detected
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Duplicate levels detected
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA
Warning: Some values were outside the color scale and will be treated as NA

Age & Gender

Race & Ethnicity

Place of Birth

plot_ly(birthplace %>% filter(Year == 2019),
        y=~County, x=~Numerator_value/Total, color=~Measure,
        type='bar') %>%
  layout(barmode = 'stack')

Economy

Unemployment

#### GO TO EXCEL SOURCE AND PULL DATA FOR CHESTER and STANLY
ggplot(unemployment) +
  geom_line(aes(x = Date, y = Unemployment, color = County))

ggplot(unemployment %>% filter(Year == 2015, Month == 6)) +
  geom_col(aes(x = County, y = Unemployment, fill = County)) + coord_flip()

Income

ggplot(income %>% filter(Year == 2014), aes(x = County, y = (Numerator_value / Total), fill = Measure), position = 'fill') +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  coord_flip()

Education

Educational Attainment

ggplot(arrange(education, Order) %>% filter(Year == 2014), aes(x = County, y = (Numerator_value / Total), fill = Order), position = 'fill') +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  coord_flip()+
  scale_fill_discrete(labels = attainment_lvl, name = '')

Health

Health Care Coverage

coverage %>% filter(Measure == "Health Insurance Total", Year == 2017) %>%
  ggplot(., aes(x = County, y = Numerator_value, fill = County))+
  geom_col() +
  scale_y_continuous(labels = comma) +
  coord_flip()
coverage %>% filter(Year == 2017, !(Measure %in% c("Health Insurance Total", "People with Health Insurance"))) %>%
  ggplot(aes(x = County, y = Numerator_value, fill = Measure, position = Measure)) +
  geom_col(position = "dodge") +
  scale_y_continuous(labels = comma) +
  coord_flip()

Housing

Housing Age

housing %>% filter(Year == 2017) %>%
  ggplot(aes(x= County, y = Year-Numerator_value, fill = County)) +
  geom_col() +
  coord_flip()

Social Well-Being

Poverty

poverty %>% filter(Year == 2010) %>%
  ggplot(., aes(x = County, y = Numerator_value/Denominator_value, fill = County)) +
  geom_col() +
  coord_flip()

Transportation

##Commuting Modes

# Way too many missing values to create a substantive visualization.
transportation %>% filter(Year == 2014) %>%
  ggplot(aes(x = County, y = Numerator_value, fill = Measure, position = Measure)) +
  geom_col(position = 'dodge') +
  scale_y_continuous(labels = comma) +
  coord_flip()